A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Abstract

With the rapid development of information technology, the sharing economy has developed rapidly all over the world as a new mode of distributing business profit, among which the bike-sharing system (BSS) has become popular in many cities because of its low cost, convenience, and environmental protection. The application of the 5th generation mobile communication technology (5G) in BSS makes users to search the bikes more accurately and quickly and enables operators to spot noncompliant bike sharing as soon as possible, significantly improving the efficiency of bike-sharing management. However, one of the thorny issues for operators is the bike-sharing rebalancing problem (BRP). It is the key to improve the efficiency of rebalancing, reduce the rebalancing cost, and realize the sustainable development of BSS on how to excavate the huge amount of customer cycling data, respond quickly to customer demand, and use intelligence optimization algorithm to rebalance bikes among stations. However, most of the previous studies dealt with only one period BRP and rarely considered multiperiod issues. At the same time, most researches have focused on minimizing the total cost or time of rebalancing or customer dissatisfaction, but few have aimed at minimizing the rebalancing amount. In addition, the demand gap can reflect the real rental and returning requirements of customers over a certain period of time, which is rarely considered in solving BRP. First of all, this paper presents a multiperiod and multiobjective bike-sharing rebalancing problem (MMBRP). Secondly, a mathematical model is formulated with the objective of minimizing both the total rebalancing cost and amount. In order to solve MMBRP, an improved multiobjective backtracking search genetic algorithm (IMBSGA) is designed. Finally, the effectiveness and competitiveness of IMBSGA in solving MMBRP are verified by numerous experiments comparing with state-of-the-art algorithms.